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  "source_url": "https://www.kaggle.com/competitions/de-lab-9-new-deadline/data",
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      "content": "## Context\nThis classic dataset contains the prices and other attributes of almost 54,000 diamonds. It's a great dataset for beginners learning to work with data analysis and visualization.\n\n## Content\n- price: price in US dollars (\\$326--\\$18,823)\n\n- carat: weight of the diamond (0.2--5.01)\n\n- cut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)\n\n- color: diamond colour, from J (worst) to D (best)\n\n- clarity: a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))\n\n- x: length in mm (0--10.74)\n\n- y: width in mm (0--58.9)\n\n- z: depth in mm (0--31.8)\n\n- depth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)\n\n- table: width of top of diamond relative to widest point (43--95)\n\n## Files\n- trainingD.csv - the training set\n- testingD.csv - the test set\n- sample_submission.csv - a sample submission file in the correct format",
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      "name": "Description",
      "content": "Analyze diamonds by their cut, color, clarity, price, and other attributes to predict the price of a diamond based on the characteristics of cut, color, clarity, and carat.\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17241140%2Fe77d583cf346740992d479fb3746f60e%2Fe40_1.png?generation=1702741598831647&alt=media)",
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      "content": "## One account per participant\n- You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n\n## Competition Timeline\n- Start Date: 17/12/2023 2:00 PM Jordan Time\n\n- End Date: 17/12/2023 4:50 PM Jordan Time\n\n",
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    {
      "name": "abstract",
      "content": "     ",
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    {
      "name": "Practice Skills",
      "content": "- Data Engineering.\n- Creative feature engineering.\n- Basic regression techniques such as Linear regression\n",
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    {
      "name": "Overview of How Kaggle’s Competitions Work",
      "content": "-  Join the Competition -> Read about the challenge description, accept the Competition Rules and gain access to the competition dataset.\n\n- Get to Work -> Download the data, build models on it locally or on Kaggle Kernels (our no-setup, customizable Jupyter Notebooks environment with free GPUs) and generate a prediction file.\n\n- Make a Submission -> Upload your prediction as a submission on Kaggle and receive an accuracy score.\n\n- Check the Leaderboard -> See how your model ranks against other Kagglers on our leaderboard.\n\n- Improve Your Score\n\n",
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    {
      "name": "Acknowledgements",
      "content": "https://www.kaggle.com/datasets/shivam2503/diamonds",
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      "name": "Evaluation",
      "content": "Goal It is your job to predict the price of diamonds. # Evaluation Metric Your score is the standard deviation of the residuals (prediction errors). This is known as Root Mean Square Error (RMSE). # Submission File Format You should submit a csv file with exactly 10788 entries plus a header row. Your submission will show an error if you have extra columns (beyond ID and price) or rows. The file should contain a header and have the following format: ``` ID,price 10788,100 ```\n",
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    {
      "name": "Citation",
      "content": "Bushra Alhijawi. (2022). Data Engineering Lab 9. Kaggle. https://kaggle.com/competitions/dataengineeringlab9\n\n\n",
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